Evaluation of tumor shape features for overall survival prognosis in glioblastoma multiforme patients

Sanghani, Parita; Ti, Ang Beng; King, Nicolas Kon Kam; Ren, Hongliang

doi:10.1016/j.suronc.2019.05.005

Cited by 19 publications

(26 citation statements)

References 22 publications

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“…This concept opens up an area of research opportunity on factors that could act as confounding variables, such as the possibility that tumors suited to complete resection are associated with a better prognosis than are highly infiltrative tumors in eloquent brain areas, the relationship of tumors with neurogenic areas, [ 76 ] tumor shape, [ 32 ] or improved functional status from surgical decompression and its association with better overall survival. [ 48 , 77 ]…”

Section: Discussionmentioning

confidence: 99%

Extent of resection and survival in patients with glioblastoma multiforme

et al. 2021

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Background: Glioblastoma multiforme (GBM) owes an ominous prognosis: its mean overall survival is 14 months. The extent of surgical resection (ESR) highlights among factors in which an association has been found to a somewhat better prognosis. However, the association between greater ESR and prolonged overall (OS) survival is not a constant finding nor a proven cause-and-effect phenomenon. To our objective is to establish the strength of association between ESR and OS in patients with GBM through a systematic review and meta-analysis. Methods: In accordance with PRISMA-P recommendations, we conducted a systematic literature search; we included studies with adult patients who had undergone craniotomy for GBM. Our primary outcome is overall postoperative survival at 12 and 24 months. We reviewed 180 studies, excluded 158, and eliminated 8; 14 studies that suited our requirements were analyzed. Results: The initial level of evidence of all studies is low, and it may be degraded to very low according to GRADE criteria because of design issues. The definition of different levels of the extent of resection is heterogeneous and poorly defined. We found a great amount of variation in the methodology of the operation and the adjuvant treatment protocol. The combined result for relative risk (RR) for OS for 12 months analysis is 1.25 [95% confidence interval (95% CI) 1.14–1.36, P < .01], absolute risk reduction (ARR) of 15.7% (95% CI 11.9–19.4), relative risk reduction (RRR) of 0.24 (95% CI 0.18–0.31), number needed to treat (NNT) 6; for 24-month analysis RR is 1.59 (95% CI 1.11–2.26, P < .01) ARR of 11.5% (95% CI 7.7–15.1), relative risk reduction (RRR) of 0.53 (95% CI 0.33–0.76), (NNT) 9. In each term analysis, the proportion of alive patients who underwent more extensive resection is significantly higher than those who underwent subtotal resection. Conclusion: Our results sustain a weak but statistically significant association between the ESR and OS in patients with GBM obtained from observational studies with a very low level of evidence according to GRADE criteria. As a consequence, any estimate of effect is very uncertain. Current information cannot sustain a cause-and-effect relationship between these variables.

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Section: Discussionmentioning

confidence: 99%

Extent of resection and survival in patients with glioblastoma multiforme

et al. 2021

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“…Although the use of radial distance-based shape features, such as the tumor boundary roughness and zero-crossing count, has shown success in classifying malignant and benign breast tumors (Kilday, Palmieri and Fox, 1993;Georgiou et al, 2007;Li et al, 2013;Rahmani Seryasat, Haddadnia and Ghayoumi Zadeh, 2016) and predicting brain tumor prognosis (Sanghani et al, 2019;Vadmal et al, 2020) from radiology (e.g. CT and MRI) images, we found that they performed poorly to characterize tumor border irregularity in pathology images.…”

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confidence: 83%

“…Crawford et al, 2020); 3) boundary descriptors based on radial distance measures (see e.g. Kilday, Palmieri and Fox, 1993;Bruce and Kallergi, 1999;Georgiou et al, 2007;Li et al, 2013;Rahmani Seryasat, Haddadnia and Ghayoumi Zadeh, 2016;Sanghani et al, 2019) and fractal dimensions (see e.g. Brú et al, 2008;Klonowski, Stepien and Stepien, 2010;Rajendran et al, 2019).…”

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confidence: 99%

Bayesian Landmark-based Shape Analysis of Tumor Pathology Images

Zhang¹,

Xiao²,

Moon³

et al. 2020

Preprint

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Medical imaging is a form of technology that has revolutionized the medical field in the past century. In addition to radiology imaging of tumor tissues, digital pathology imaging, which captures histological details in high spatial resolution, is fast becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods facilitate the segmentation of tumor regions at almost the cellular level from digital pathology images. The traditional shape features that were developed for characterizing tumor boundary roughness in radiology are not applicable. Reliable statistical approaches to modeling tumor shape in pathology images are in urgent need. In this paper, we consider the problem of modeling a tumor boundary with a closed polygonal chain. A Bayesian landmark-based shape analysis (BayesLASA) model is proposed to partition the polygonal chain into mutually exclusive segments to quantify the boundary roughness piecewise. Our fully Bayesian inference framework provides uncertainty estimates of both the number and locations of landmarks. The BayesLASA outperforms a recently developed landmark detection model for planar elastic curves in terms of accuracy and efficiency. We demonstrate how this model-based analysis can lead to sharper inferences than ordinary approaches through a case study on the 246 pathology images from 143 non-small cell lung cancer patients. The case study shows that the heterogeneity of tumor boundary roughness predicts patient prognosis (p-value < 0.001). This statistical methodology not only presents a new model for characterizing a digitized object's shape features by using its landmarks, but also provides a new perspective for understanding the role of tumor surface in cancer progression.

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“…19 Afterward, researchers combined clinical data with imaging features. 22 Nowadays, researchers have explored volumetric, texture and tumor shape-based features that have achieved good results for OS-time prediction. 18,23 The texture features of MRI allow us to compute statistical features at multiscales 24 ; high-frequency components are extracted using small scales while large scales capture low-frequency components.…”

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation and multiview multiscale‐based radiomic model for patient's overall survival prediction

Fiaz

Madni

Anwar

et al. 2021

Int J Imaging Syst Tech

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A brain tumor is the most common primary brain malignancy. Delaying in brain tumor diagnosis is a primary cause of death in affected individuals. Therefore, early diagnosis of a brain tumor is essential for treatment planning and prognosis. In this study, the multilevel dilated convolutional neural network (MLDCNN) model is used for brain tumor segmentation. MLDCNN model is implemented independently for five MLDC blocks with a different combination of dilation rates to analyze their impact on brain tumor segmentation. For each segmentation model, overall survival time prediction is performed independently. An automated system is proposed for the overall survival time prediction of patients suffering from a brain tumor. First, shape and multiscale texture‐based features are extracted from LoG filtered and wavelet decomposed images of the magnetic resonance imaging scans. The proposed model utilizes 3D information by extracting radiomic features from axial, coronal, and sagittal views. These features are reduced using an extra tree classifier to avoid overfitting. Random forest algorithms are applied on selected feature sets to predict overall survival time in days. Extensive experimentation is performed for the segmentation and survival time prediction on the publicly available BraTS2019 and BraTS 2020 datasets. Results demonstrate that the proposed approach achieved the least mean squared error value in the survival time prediction task.

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Evaluation of tumor shape features for overall survival prognosis in glioblastoma multiforme patients

Cited by 19 publications

References 22 publications

Extent of resection and survival in patients with glioblastoma multiforme

Extent of resection and survival in patients with glioblastoma multiforme

Bayesian Landmark-based Shape Analysis of Tumor Pathology Images

Brain tumor segmentation and multiview multiscale‐based radiomic model for patient's overall survival prediction

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